st: Discrete choice in MATA

Hello,
I am relatively new at MATA and I am trying to program maximum likelihood estimation for my discrete choice model.
The labour supply model allows for 6 labour supply alternatives at the individual level and an example is included below for an individual:
Id h y_1 l
1 0 2 80
1 8 4 72
1 16 6 64
1 24 8 56
1 32 10 48
1 40 12 40
The model can be estimated by maximum likelihood, where the log likelihood is as follows: ln(L) = ln{ exp(xb)/sum(exp(xb)) } over all individuals.
This works well in STATA by using ml model but I now want to program it in MATA. The difficulty here is constructing the denominator, i.e the summation of exp(xb) over all alternatives.
My code is as follows:
mata
st_view(X=0,.,("lny_1", "lnl"))
st_view(y=0,.,("choice"))
void logistic(todo, p, y, X, lf, g, H)
{
b = p[1, (1::cols(X))]' /* Transpose such that b is a column vector*/
denom = colsum(exp(X*b)) /*HERE I NEED TO SUM OVER THE ALTERNATIVES PER INDIVIDUAL , WHICH DOES NOT WORK FOR ME*/
lf = y' * ln(exp(X*b)/denom)
}
S = optimize_init()
optimize_init_evaluator(S, &logistic())
optimize_init_params(S, (0, 0)) /* One extra element. Starting values are (0, 0)*/
optimize_init_argument(S, 1, y)
optimize_init_argument(S, 2, X)
p = optimize(S)
p
end
Can anyone please help me out?
Henk-Wim de Boer
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